A FASHION PRODUCT RECOMMENDATION BASED ON ADAPTIVE VPKNN-NET ALGORITHM WITHOUT FUZZY SIMILAR IMAGE

Sabitha R^1*Sundar D²

• ¹V.V. Vanniaperumal College for Women,, Virudhunagar, India
• ²Government Arts College Melur, Madurai, Tamil Nadu, India

Recommender systems play a critical role in e-commerce by helping users navigate vast product catalogs and discover relevant items. However, traditional keyword-based systems often fall short in visually driven domains like fashion, where subjective style preferences are difficult to express textually. This research aims to develop a visually aware fashion product recommendation framework using an Adaptive VPKNN-net algorithm. The proposed method integrates deep visual feature extraction via a pre-trained VGG16 Convolutional Neural Network and dimensionality reduction through Principal Component Analysis (PCA). It employs a modified K-Nearest Neighbors (KNN) algorithm that combines Euclidean and cosine similarity metrics to improve visual similarity assessments. Unlike fuzzy logic-based approaches, the proposed method offers a precise and interpretable recommendation mechanism. Experiments conducted on the Fashion Product Images (Small) dataset from Kaggle demonstrate that the Adaptive VPKNN-net model achieves superior performance, with a classification accuracy of 98.69%, and lower RMSE (0.8213) and MAE (0.6045) compared to baseline models. In conclusion, the proposed framework effectively enhances the accuracy, efficiency, and interpretability of fashion product recommendations, offering a scalable solution for visually driven e-commerce platforms with limited training data.